Information processing method and information processing system

ABSTRACT

An information processing method includes: deciding a configuration of a machine learning model, using a processor; performing first determination as to whether the machine learning model in the decided configuration meets a first performance requirement on hardware performance; performing learning using the machine learning model in the configuration determined to meet the first performance requirement, performing second determination as to whether a learned model obtained by the learning meets a second performance requirement on evaluation value of output of a machine learning model, and when the learned model is determined to meet the second performance requirement, outputting information indicating that both performance requirements are met; and when it is determined that the first performance requirement is not met, changing the configuration of the machine learning model, and performing the first determination as to whether the machine learning model in the changed configuration meets the first performance requirement.

BACKGROUND 1. Technical Field

The present disclosure relates to an information processing method andan information processing system that decides a machine learning modelused for machine learning.

2. Description of the Related Art

Japanese Unexamined Patent Application Publication No. 2017-97807discloses a learning method in which in learning using a neural network,processing to change the number of units in a neural network isperformed and the learning is stopped by using a genetic algorithm.

SUMMARY

In one general aspect, the techniques disclosed here feature aninformation processing method including: deciding a configuration of amachine learning model, using a processor; performing firstdetermination using the processor as to whether the machine learningmodel in the decided configuration meets a first performance requirementwhich is a requirement on hardware performance; when it is determinedthat the first performance requirement is met in the firstdetermination, performing learning using the processor and the machinelearning model in the configuration determined to meet the firstperformance requirement, performing second determination using theprocessor as to whether a learned model obtained by the learning meets asecond performance requirement which is a requirement on evaluationvalue of output of a machine learning model, and when the learned modelis determined to meet the second performance requirement in the seconddetermination, outputting information using the processor, theinformation indicating that the first performance requirement and thesecond performance requirement are met; and when it is determined thatthe first performance requirement is not met in the first determination,changing the configuration of the machine learning model using theprocessor, and performing the first determination as to whether themachine learning model in the changed configuration meets the firstperformance requirement.

It should be noted that general or specific embodiments may beimplemented as a system, a device, an integrated circuit, a computerprogram, a storage medium such as a computer-readable CD-ROM, or anyselective combination thereof.

An information processing method according to the present disclosure iscapable of efficiently deciding a machine learning model that meetsrequested hardware performance requirements.

Additional benefits and advantages of the disclosed embodiments willbecome apparent from the specification and drawings. The benefits and/oradvantages may be individually obtained by the various embodiments andfeatures of the specification and drawings, which need not all beprovided in order to obtain one or more of such benefits and/oradvantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram for explaining the outline of an informationprocessing system according to an embodiment;

FIG. 2 is a block diagram illustrating an example of a hardwareconfiguration of the information processing device according to theembodiment;

FIG. 3 is a block diagram illustrating an example of a hardwareconfiguration of an input/output device;

FIG. 4 is a block diagram illustrating an example of a functionalconfiguration of an information processing system;

FIG. 5 is a flowchart illustrating an example of an informationprocessing method in the information processing system;

FIG. 6 is a flowchart illustrating an example of details of decisionprocessing according to the embodiment; and

FIG. 7 is a flowchart illustrating an example of details of decisionprocessing according to the embodiment.

DETAILED DESCRIPTION

(Underlying Knowledge Forming Basis of the Present Disclosure)

The present inventor has found that the following problems occur in thelearning method described in “Description of the Related Art”.

In recent years, in order to secure the safety of advanced driverassistance system (ADAS) and an automatic operating system, processingusing machine learning such as deep learning needs to be applied to anin-vehicle system. Also, in order to utilize as an in-vehicle electroniccontrol unit (ECU), a processor that performs processing using suchmachine learning is required to meet stringent constraints on powerconsumption, processing speed, and recognition accuracy.

However, processors having many different features are used forin-vehicle ECUs according to vehicle types, grades of automobile. Forthis reason, a problem arises in that it is difficult to efficientlydecide a machine learning model that meets the above-mentionedconstraints for different processors.

In other words, in Japanese Unexamined Patent Application PublicationNo. 2017-97807, the performance of hardware utilizing a machine learningresult is not taken into consideration, and thus it is difficult toefficiently decide a machine learning model that meets the performancerequirements on the hardware.

The present disclosure provides an information processing method and aninformation processing system that are capable of efficiently decide amachine learning model that meets requested hardware performancerequirements.

A information processing method according to an aspect of the presentdisclosure is an information processing method including: deciding aconfiguration of a machine learning model, using a processor; performingfirst determination using the processor as to whether the machinelearning model in the decided configuration meets a first performancerequirement which is a requirement on hardware performance; when it isdetermined that the first performance requirement is met in the firstdetermination, performing learning using the processor and the machinelearning model in the configuration determined to meet the firstperformance requirement, performing second determination using theprocessor as to whether a learned model obtained by the learning meets asecond performance requirement which is a requirement on evaluationvalue of output of a machine learning model, and when the learned modelis determined to meet the second performance requirement in the seconddetermination, outputting information using the processor, theinformation indicating that the first performance requirement and thesecond performance requirement are met; and when it is determined thatthe first performance requirement is not met in the first determination,changing the configuration of the machine learning model using theprocessor, and performing the first determination as to whether themachine learning model in the changed configuration meets the firstperformance requirement.

According to this, the configuration of a machine learning model ischanged without performing the second determination until the firstperformance requirement is met in the first determination, and thus itis possible to reduce the number of times of performing the seconddetermination. Therefore, it is possible to efficiently decide a machinelearning model that meets requested hardware performance requirements.In addition, it is possible to reduce the processing load andconsumption energy of processors.

Preferably, in the information processing method of the presentdisclosure, when it is determined that the second performancerequirement is not met in the second determination, the learning isperformed using a parameter different from a parameter used in thepreviously performed learning, the second determination is performed asto whether a learned model obtained by the learning using the differentparameter meets the second performance requirement, when a number ofdeterminations, in which it is determined that the second performancerequirement is not met, is greater than or equal to a first number, theconfiguration of the machine learning model is changed, and the firstdetermination is performed as to whether the machine learning model inthe changed configuration meets the first performance requirement.

According to this, when it is determined the first number of times ormore that the second performance requirement is not met, theconfiguration of the machine learning model is changed, and thus thenumber of times of learning can be reduced. Therefore, it is possible toefficiently decide a machine learning model.

Preferably, in the information processing method of the presentdisclosure, weight reduction processing is further performed on thelearned model determined to meet the second performance requirement inthe second determination, third determination is performed as to whethera weight reduction model obtained by the weight reduction processingmeets a third performance requirement which is a requirement on hardwareperformance, and when it is determined that the third performancerequirement is met in the third determination, information indicatingthat the third performance requirement is met is output.

According to this, the configuration of the machine learning model ischanged without performing the second determination, the weightreduction processing, and the third determination until the firstperformance requirement is met in the first determination. Therefore, itis possible to reduce the number of times of performing the seconddetermination, the weight reduction processing, and the thirddetermination.

Preferably, in the information processing method of the presentdisclosure, a condition for hardware performance, specified in the thirdperformance requirement is more stringent than a condition for hardwareperformance, specified in the first performance requirement.

Therefore, it is possible to reduce the number of times of decision thatthe first performance requirement is not met, decision of theconfiguration of the machine learning model, and performing the firstdetermination. Also, more stringent determination is performed in thethird determination, thereby making it possible to reduce the number oftimes of performing subsequent processing to the third determination,for instance.

Preferably, in the information processing method of the presentdisclosure, when hardware performance of the weight reduction modeldetermined not to meet the third performance requirement in the thirddetermination meets a first condition, the weight reduction processingis performed using a parameter different from a parameter used in thepreviously performed weight reduction processing, and the thirddetermination is performed as to whether a weight reduction modelobtained by the weight reduction processing using the differentparameter meets the third performance requirement.

According to this, even when the third performance requirement is notmet, when the current performance meets a requirement close to the thirdperformance requirement, the weight reduction processing is performedwith a changed parameter. Therefore, it is possible to efficientlydetermine a weight reduction model that meets the third performancerequirement.

Preferably, in the information processing method of the presentdisclosure, (i) when hardware performance of the weight reduction modeldetermined not to meet the third performance requirement in the thirddetermination does not meet the first condition, or (ii) when a numberof determinations, in which it is determined that the third performancerequirement is not met, is greater than or equal to a second number, theconfiguration of the machine learning model is changed, and the firstdetermination is performed as to whether the machine learning model inthe changed configuration meets the first performance requirement.

According to this, when the third performance requirement is not met,and the number of determinations, in which the third performancerequirement is not met, is greater than or equal to the second number oftimes, the configuration of the machine learning model is changed.Therefore, it is possible to reduce the number of times of performingthe weight reduction processing. Thus, it is possible to efficientlydecide a machine learning model.

Preferably, in the information processing method of the presentdisclosure, fourth determination is performed as to whether the weightreduction model determined to meet the third performance requirement inthe third determination meets a fourth performance requirement which isa requirement on hardware performance of real machine, and when it isdetermined that the fourth performance requirement is met in the fourthdetermination, information indicating that the fourth performancerequirement is met is output.

According to this, the fourth determination is performed on the weightreduction model which has been determined to meet the third performancerequirement in the third determination, and thus it is possible toreduce the number of times of performing the fourth determination.

Preferably, in the information processing method of the presentdisclosure, when the hardware performance of the weight reduction modeldetermined not to meet the fourth performance requirement in the fourthdetermination meets a second condition, the weight reduction processingis performed using a parameter different from a parameter used in thepreviously performed weight reduction processing.

According to this, even when the fourth performance requirement is notmet, when the current performance meets a requirement close to thefourth performance requirement, the weight reduction processing isperformed with a changed parameter, thus it is possible to efficientlydetermine a weight reduction model that meets the fourth performancerequirement.

Preferably, in the information processing method of the presentdisclosure, (i) when the hardware performance of the weight reductionmodel determined not to meet the fourth performance requirement in thefourth determination does not meet the second condition, or (ii) when anumber of determinations, in which it is determined that the fourthperformance requirement is not met, is greater than or equal to a thirdnumber, the configuration of the machine learning model is changed, andthe first determination is performed as to whether the machine learningmodel in the changed configuration meets the first performancerequirement.

According to this, when the fourth performance requirement is not met,and the number of determinations, in which the fourth performancerequirement is not met, is greater than or equal to the third number oftimes, the configuration of the machine learning model is changed.Therefore, it is possible to reduce the number of times of performingthe weight reduction processing. Thus, it is possible to efficientlydecide a machine learning model.

Preferably, in the information processing method of the presentdisclosure, fifth determination is performed as to whether the weightreduction model determined to meet the fourth performance requirement inthe fourth determination meets a fifth performance requirement which isa requirement on evaluation value of output of a machine learning model,and when it is determined that the fifth performance requirement is met,information indicating that the fifth performance requirement is met isoutput.

According to this, the fifth determination is performed on the weightreduction model which has been determined to meet the fourth performancerequirement in the fourth determination, and thus it is possible toreduce the number of times of performing the fifth determination.

Preferably, in the information processing method of the presentdisclosure, when an evaluation value of the weight reduction modeldetermined not to meet the fifth performance requirement in the fifthdetermination meets a third condition, the weight reduction processingis performed using a parameter different from a parameter used in thepreviously performed weight reduction processing.

According to this, even when the fifth performance requirement is notmet, when the current performance meets a requirement close to the fifthperformance requirement, the weight reduction processing is performedwith a changed parameter, thus it is possible to efficiently determine aweight reduction model that meets the fifth performance requirement.

Preferably, in the information processing method of the presentdisclosure, (i) when the evaluation value of the weight reduction modeldetermined not to meet the fifth performance requirement in the fifthdetermination does not meet the third condition, or (ii) when a numberof determinations, in which it is determined that the fifth performancerequirement is not met, is greater than or equal to a fourth number, theconfiguration of the machine learning model is changed, and the firstdetermination is performed as to whether the machine learning model inthe changed configuration meets the first performance requirement.

According to this, even when the weight reduction processing isperformed with a changed parameter, and the number of determinations, inwhich an obtained weight reduction model does not meet the fifthperformance requirement, is greater than or equal to the fourth numberof times, the configuration of the machine learning model is changed.Therefore, it is possible to reduce the number of times of performingthe weight reduction processing. Thus, it is possible to efficientlydecide a machine learning model.

It should be noted that general or specific embodiments may beimplemented as a system, a device, an integrated circuit, a computerprogram, a storage medium such as a computer-readable CD-ROM, or anyselective combination thereof.

Hereinafter, an information processing method and an informationprocessing system according to an aspect of the present disclosure willbe specifically described with reference to the drawings.

It is to be noted that each of the embodiments described below presentsa specific example of the present disclosure. The numerical values,shapes, components, steps, and order of the steps that are presented inthe following embodiments are examples are not intended to limit thepresent disclosure. Those components in the following embodiments, whichare not stated in the independent claim that defines the most genericconcept are each described as an arbitrary component.

Embodiment

Hereinafter, an embodiment will be described with reference to FIGS. 1to 7.

[1-1. Configuration]

FIG. 1 is a diagram for explaining the outline of an informationprocessing system according to an embodiment.

An information processing system 1 includes an information processingdevice 100, an input/output device 200, and a processor 300. Informationprocessing system 1 is a system that decides an appropriate machinelearning model for a designated processor.

In the information processing system 1, the information processingdevice 100 is coupled to the input/output device 200 to allowcommunication therebetween via a communication network. The informationprocessing device 100 is, for instance, a server. The input/outputdevices 200 is, for instance, a desktop personal computer (PC), a tabletterminal, or a laptop PC. Also, the communication network may be, forinstance, a general-purpose communication network such as the Internet,or a dedicated communication network. The processor 300 is the processorof a real machine to be tested, and includes multiple types ofprocessors.

The information processing device 100 obtains from the input/outputdevice 200 hardware information indicating the hardware performance of aprocessor, and test requirement information indicating the performancerequirements of the processor and a machine learning model. The detailsof the performance requirements will be described later. The informationprocessing device 100 then determines a machine learning model byperforming the later-described decision processing based on the hardwareinformation and the test requirement information obtained from theinput/output device 200, and outputs the decided machine learning modelto the input/output device 200.

It is to be noted that the information processing device 100 may be acomponent of a cloud computing system, and may be coupled to theinput/output device 200 via the Internet or the like.

[1-2. Hardware Configuration]

The hardware configuration of the information processing device 100 willbe described with reference to FIG. 2.

FIG. 2 is a block diagram illustrating an example of a hardwareconfiguration of the information processing device according to theembodiment.

As illustrated in FIG. 2, the information processing device 100 includesa central processing unit (CPU) 101, a main memory 102, a storage 103,and communication interface (IF) 104 and, a graphics processing unit(GPU) 105 as the hardware configuration.

The CPU 101 is a processor that executes a control program stored in thestorage 103.

The main memory 102 is a volatile storage area that is used as a workarea when the CPU 101 executes the control program.

The storage 103 is a non-volatile storage area that stores the controlprogram, and contents, for instance.

The communication IF 104 is a communication interface that communicateswith the input/output device 200 via a communication network. Thecommunication IF 104 is, for instance, a wired LAN interface. Thecommunication IF 104 may be a wireless LAN interface. Also, thecommunication IF 104 is not limited to only a LAN interface, but alsomay be any type of communication IF as long as the communication IF 104can establish communication connection with a communication network.

The GPU 105 is a processor that performs processing of machine learning.

The hardware configuration of the input/output device 200 will bedescribed with reference to FIG. 3.

FIG. 3 is a block diagram illustrating an example of the hardwareconfiguration of the input/output device.

As illustrated in FIG. 3, the input/output device 200 includes a centralprocessing unit (CPU) 201, a main memory 202, a storage 203, a display204, an input interface (IF) 205, and a communication IF 206 as thehardware configuration. In other words, the input/output device 200 maybe called as an information processing device other than the informationprocessing device 100.

The CPU 201 is a processor that executes a control program stored in thestorage 203.

The main memory 202 is a volatile storage area that is used as a workarea when the CPU 201 executes the control program.

The storage 203 is a non-volatile storage area that stores the controlprogram, and contents, for instance.

The display 204 is a display device that displays a video including animage. For instance, the display 204 is a liquid crystal display, or anorganic EL display.

The input IF 205 is an interface for receiving input from a user. Theinput IF 205 may be a pointing device such as a mouse, a touch-pad, atouch panel, and a trackball, or may be a keyboard.

The communication IF 206 is a communication interface that communicateswith the information processing device 100 via a communication network.The communication IF 206 may be, for instance, a wired LAN interface, ora wireless LAN interface. Also, the communication IF 206 is not limitedto only a LAN interface, but also may be any type of communication IF aslong as the communication IF 104 can establish communication connectionwith a communication network.

[1-3. Functional Configuration]

Next, the functional configuration of the information processing system1 will be described with reference to FIG. 4.

FIG. 4 is a block diagram illustrating an example of the functionalconfiguration of the information processing system.

First, the functional configuration of the input/output device 200 willbe described.

The information processing system 1 includes a controller 120, a decider130, a model configuration requirement database (DB) 131, a determiner140, a test requirement database (DB) 141, a learner 150, and a weightreducer 160 as the functional configuration. The information processingsystem 1 may further include a display 111, an input receiver 112, andan interrupter 151.

The display 111 displays an user interface (UI) to identify the hardwareinformation of a processor for which a machine learning model is to beused, and test requirement information indicating performancerequirement. In addition, the display 111 may display UI for notifying auser that hardware information and test requirement information selectedaccording to input received by the input receiver 112 have beentransmitted to the information processing device 100. Also, the display111 may display information on a machine learning model decided by theinformation processing device 100. The display 111 is implemented, forinstance, by the CPU 201, the main memory 202, the storage 203, and thedisplay 204 of the input/output device 200.

The input receiver 112 receives input for an UI displayed on the display111. Specifically, the input receiver 112 receives informationindicating hardware information on a processor for which a machinelearning model is to be used, and test requirement information for theUI to identify the hardware information and the test requirementinformation displayed on the display 111. The input receiver 112 outputsto the controller 120 hardware information identified according to areceived input. Also, the input receiver 112 outputs test requirementinformation generated according to the received input to the testrequirement DB 141, and the test requirement information is stored inthe test requirement DB 141. It is to be noted that the test requirementinformation may not be generated according to input via the inputreceiver 112, and may be pre-stored in the test requirement DB 141. Inaddition, the test requirement information stored in the testrequirement DB 141 may be changed according to information obtained froman external device.

For instance, the input receiver 112 may receive input of identificationinformation such as a model number for identifying a processor, and mayidentify hardware information associated with the receivedidentification information among association information pieces in whichidentification information and hardware information on processor areassociated with each other. The input/output device 200 may obtain anassociation information piece from an external information processingdevice via a communication network, or may utilize the associationinformation pieces pre-stored in the storage 203. For instance, theinput receiver 112 may directly receive hardware information on aprocessor. The hardware information is information that indicates, forinstance, the processing speed and power consumption of a processor.

For instance, the input receiver 112 receives requirement on processingspeed and power consumption, in short, requirement on hardwareperformance. The requirement on processing speed is, for instance, thata total time taken for predetermined calculation processing and memorytransfer processing is shorter than a reference time. The requirement onpower consumption is, for instance, that the power consumption used forperforming predetermined processing is lower than a predeterminedreference power.

For instance, the input receiver 112 receives requirement on accuracyrate of output of a machine learning model, in short, requirement onevaluation value. For instance, the requirement on accuracy rate is thatan accuracy rate when a predetermined test is conducted is higher than areference accuracy rate.

The test requirement information includes the first to fifth performancerequirements which serve as respective references of the first to fifthdetermination made by the determiner 140, and different values may beset to respective requirements. The first performance requirement, thethird performance requirement, and the fourth performance requirementare requirements on hardware performance, for instance, the performanceis higher than a threshold defined for at least one of processing speed,power consumption, memory transfer amount, and calculation amount perunit time. The second performance requirement and the fifth performancerequirement are, for instance, requirements on evaluation value ofoutput of a machine learning model, specifically for instance, theperformance is higher than a threshold defined for at least one ofaccuracy rate, precision, recall, F value, error, and incorrect rate.

The input receiver 112 is implemented, for instance, by the input IF 205and the communication IF 206 of the input/output device 200.

The controller 120 performs decision processing for deciding a machinelearning model by controlling the decider 130, the determiner 140, thelearner 150, and the weight reducer 160. Specifically, the controller120 performs the later-described decision processing on theconfiguration of a machine learning model temporarily decided by thedecider 130. The controller 120 decides the processing to be performednext in the decision processing, using determination results of thefirst to fifth determination made by the determiner 140. The details ofdeciding the processing to be performed next by the controller 120 willbe described later. The controller 120 is implemented, for instance, bythe CPU 101, the main memory 102, the storage 103, and the communicationIF 104 of the information processing device 100.

The decider 130 decides the configuration of a machine learning model.Specifically, as the configuration of the machine learning model usedfor decision processing, the decider 130 decides one of theconfigurations of multiple machine learning models having differentconfigurations stored in the model configuration requirement DB 131. Themachine learning model decided by the decider 130 is, for instance, thenetwork configuration of a neural network. The configuration of a neuralnetwork varies with, for instance, the number of hierarchies and thenumber of units at each hierarchy.

Also, according to an instruction from the controller 120, the decider130 may decide one of the configurations of multiple machine learningmodels stored in the model configuration requirement DB 131 as theconfiguration of the machine learning model used for decisionprocessing, the one being excluded from the configurations of themachine learning model once decided. In other words, according to aninstruction from the controller 120, the decider 130 may change theconfiguration of the machine learning model used for decision processingfrom the configuration of a machine learning model already decided tothe configuration of another machine learning model. The decider 130changes the configuration of the machine learning model, for instance bychanging the number of hierarchies or the number of units. When thememory transfer amount of the machine learning model is found to exceedthe first performance requirement, the third performance requirement, orthe fourth performance requirement by the test of the determiner 140,the decider 130 reduces the number of hierarchies or the number ofunits. Similarly, when one of the processing speed, power consumption,calculation amount per unit time is exceeded, the number of hierarchiesor the number of units is reduced.

When the configuration of the machine learning model is changed, thedecider 130 may decide the configuration of the next machine learningmodel according to a sequence predetermined for the configurations ofmultiple machine learning models stored in the model configurationrequirement DB 131, or may decide the configuration of the next machinelearning model at random. When the configuration of the machine learningmodel is changed, the decider 130 may also decide the configuration ofthe next machine learning model according to a predetermined algorithmusing determination results of the first to fifth determination made bythe determiner 140 obtained by the controller 120.

The decider 130 outputs to the controller 120 the configuration of themachine learning model decided to be used in the decision processing.The decider 130 is implemented, for instance, by the CPU 101, the mainmemory 102, and the storage 103 of the information processing device100. Also, the model configuration requirement DB 131 is implemented,for instance, by the storage 103 of the information processing device100.

According to an instruction from the controller 120, the determiner 140performs first determination as to whether or not the machine learningmodel decided to be used in the decision processing by the decider 130meets the first performance requirement. The determiner 140, whenperforming the first determination, obtains the first performancerequirement from the test requirement DB 141. The determiner 140performs the first determination using the obtained the firstperformance requirement. The test requirement DB 141 includes the firstperformance requirement. The first performance requirement is that theperformance is higher than a threshold T11. Here, a description is givenusing an example in which the first performance requirement is forprocessing speed.

Thus, in the first determination, the determiner 140 determines whetheror not the processing speed calculated from the configuration of themachine learning model decided by the decider 130 exceeds the thresholdT11, and when the processing speed exceeds the threshold T11, determinesthat the first performance requirement is met. In this manner, when agreater value indicates higher hardware performance in the firstdetermination, the determiner 140 determines whether or not the valueexceeds the threshold defined in the first performance requirement, andwhen the value exceeds the threshold, determines that the firstperformance requirement is met.

When the first determination is performed for power consumption, thedeterminer 140 determines whether or not the power consumption is lessthan the threshold defined in the first performance requirement on powerconsumption. This is because a lower power consumption indicates higherperformance. That is, when a smaller value indicates higher hardwareperformance in the first determination, the determiner 140 determineswhether or not the value is less than the threshold defined in the firstperformance requirement, and when the value is less than the threshold,determines that the first performance requirement is met.

The determiner 140 outputs a determination result in the firstdetermination to the controller 120.

Also, according to an instruction from the controller 120, thedeterminer 140 performs second determination as to whether or not alearned model obtained from learning by the learner 150 meets the secondperformance requirement. When performing the second determination, thedeterminer 140 obtains the second performance requirement from the testrequirement DB 141, and performs the second determination using theobtained second performance requirement. The test requirement DB 141includes the second performance requirement. The second performancerequirement is that the performance is higher than a threshold T21.Here, a description is given using an example in which the secondperformance requirement is for accuracy rate.

Thus, in the second determination, the determiner 140 determines whetheror not the accuracy rate calculated as the evaluation value of output ofthe learned model exceeds the threshold T21. In this manner, when alarger evaluation value indicates higher performance in the seconddetermination, the determiner 140 determines whether or not the valueexceeds the threshold defined in the second performance requirement, andwhen the value exceeds the threshold, determines that the secondperformance requirement is met. It is to be noted that when a smallerevaluation value indicates higher performance in the seconddetermination, the determiner 140 determines whether or not theevaluation value is less than the threshold, and when the evaluationvalue is less than the threshold, determines that the second performancerequirement is met.

The determiner 140 outputs a determination result in the seconddetermination to the controller 120.

Also, according to an instruction from the controller 120, thedeterminer 140 may perform third determination as to whether or not aweight reduction model obtained by the weight reduction processingperformed by the weight reducer 160 meets the third performancerequirement. When performing the third determination, the determiner 140obtains the third performance requirement from the test requirement DB141. The determiner 140 then performs the third determination using theobtained third performance requirement. The test requirement DB 141includes the third performance requirement. The third performancerequirement is that the performance is higher than a threshold T31.Here, a description is given using an example in which the thirdperformance requirement is for processing speed. Also, the condition forhardware performance specified in the third performance requirement maybe more stringent than the condition for hardware performance specifiedin the first performance requirement. Specifically, when a larger valueindicates higher hardware performance, the threshold T31 is greater thanthe threshold T11, and when a smaller value indicates higher hardwareperformance, the threshold T31 is less than the threshold T11.

As an example, when the third performance requirement is for processingspeed, in the third determination, the determiner 140 determines whetheror not a processing speed calculated from the weight reduction modelobtained by the weight reduction processing exceeds the threshold T31,and when the processing speed exceeds the threshold T31, determines thatthe third performance requirement is met. In this manner, when a largervalue indicates higher hardware performance in the third determination,the determiner 140 determines whether or not the value exceeds thethreshold defined in the third performance requirement, and when thevalue exceeds the threshold, determines that the third performancerequirement is met. Similarly to the first determination, when a smallervalue indicates higher hardware performance in the third determination,the determiner 140 determines whether or not the value is less than thethreshold defined in the third performance requirement, and when thevalue is less than the threshold, determines that the third performancerequirement is met.

The determiner 140 outputs a determination result in the thirddetermination to the controller 120.

Also, according to an instruction from the controller 120, thedeterminer 140 may perform fourth determination as to whether or not theweight reduction model determined to meet the third performancerequirement in the third determination meets the fourth performancerequirement. When performing the fourth determination, the determiner140 obtains the fourth performance requirement from the test requirementDB 141. The determiner 140 then performs the fourth determination usingthe obtained fourth performance requirement. The determiner 140 performsthe fourth determination on a processor which is among connectedprocessors 300 and corresponds to the hardware information obtained bythe controller 120. The fourth performance requirement is for thehardware performance of the processor 300 of a real machine, is that theperformance is higher than a threshold T41. Also, the condition forhardware performance specified in the fourth performance requirement maybe more stringent than the condition for hardware performance specifiedin the third performance requirement. Specifically, when a larger valueindicates higher hardware performance, the threshold T41 is greater thanthe threshold T31, and when a smaller value indicates higher hardwareperformance, the threshold T41 is less than the threshold T31.

As an example, when the fourth performance requirement is for processingspeed, in the fourth determination, the determiner 140 determineswhether or not a processing speed calculated from the weight reductionmodel determined to meet the third performance requirement in the thirddetermination exceeds the threshold T41, and when the processing speedexceeds the threshold T41, determines that the fourth performancerequirement is met. In this manner, when a larger value indicates higherhardware performance in the fourth determination, the determiner 140determines whether or not the value exceeds the threshold defined in thefourth performance requirement, and when the value exceeds thethreshold, determines that the fourth performance requirement is met.Similarly to the first and third determination, when a smaller valueindicates higher hardware performance in the fourth determination, thedeterminer 140 determines whether or not the value is less than thethreshold defined in the fourth performance requirement, and when thevalue is less than the threshold, determines that the fourth performancerequirement is met.

The determiner 140 outputs a determination result in the fourthdetermination to the controller 120.

Also, according to an instruction from the controller 120, thedeterminer 140 may perform the fifth determination as to whether or notthe weight reduction model determined to meet the fourth performancerequirement in the fourth determination meets the fifth performancerequirement. Specifically, when the determiner 140 conducts apredetermined test for the processor 300 of the real machine using theweight reduction model determined to meet the fourth performancerequirement in the fourth determination, the determiner 140 performs thefifth determination as to whether the fifth performance requirement ismet. When performing the fifth determination, the determiner 140 obtainsthe fifth performance requirement from the test requirement DB 141, andperforms the fifth determination using the obtained fifth performancerequirement. The test requirement DB 141 includes the fifth performancerequirement. The fifth performance requirement is that the performanceis higher than a threshold T51. Here, a description is given using anexample in which the fifth performance requirement is for accuracy rate.

Thus, in the fifth determination, the determiner 140 determines whetheror not the accuracy rate calculated as the evaluation value of output ofthe learned model exceeds the threshold T51. In this manner, when alarger evaluation value indicates higher performance in the fifthdetermination, the determiner 140 determines whether or not the valueexceeds the threshold defined in the fifth performance requirement, andwhen the value exceeds the threshold, determines that the fifthperformance requirement is met. It is to be noted that when a smallerevaluation value indicates higher performance in the fifthdetermination, the determiner 140 determines whether or not the value isless than the threshold, and when the value is less than the threshold,determines that the fifth performance requirement is met.

The determiner 140 outputs a determination result in the fifthdetermination to the controller 120.

The determiner 140 is implemented, for instance, by the CPU 101, themain memory 102, and the storage 103 of the information processingdevice 100. Also, the test requirement DB 141 is implemented, forinstance, by the storage 103 of the information processing device 100.

According to an instruction from the controller 120, the learner 150performs learning using a machine learning model in a configurationwhich has been determined to meet the first performance requirement. Thelearner 150 performs learning for recognition processing used for ADAS,an automatic operation system, for instance. The learner 150 isimplemented, for instance, by the CPU 101, the main memory 102, and thestorage 103 of the information processing device 100.

The interrupter 151 obtains a learning situation of the learner 150, andwhen the accuracy (error) does not converge in an early stage oflearning, learning by the learner 150 is interrupted. The interrupter151 is implemented, for instance, by the CPU 101, the main memory 102,and the storage 103 of the information processing device 100.

The weight reducer 160 performs weight reduction processing on a learnedmodel which has been determined to meet the second performancerequirement in the second determination. The weight reducer 160performs, for instance, weight quantization processing, or weightpruning processing, as the weight reduction processing. The weightquantization processing is quantization processing, for instance, from asingle precision floating point number (32 bits) or a double precisionfloating point number (64 bits) to a half precision floating pointnumber (16 bits) or a fixed point number (any number of bits from 16bits, 8 bits, and 64 bits to 1 bit). The weight pruning processing isprocessing to eliminate the weight coefficient of a neural network whosecontribution to output is low. The weight reducer 160 is implemented,for instance, by the CPU 101, the main memory 102, and the storage 103of the information processing device 100.

[1-4. Operation]

Next, the operation of the information processing system 1 according tothe embodiment will be described.

FIG. 5 is a flowchart illustrating an example of an informationprocessing method in the information processing system.

First, a user operates the input/output device 200 to execute anapplication for deciding an optimal machine learning model for apredetermined processor in the input/output device 200. Thus, in theinput/output device 200, an UI for identifying the hardware informationand test requirement information on a processor as a processing targetis displayed on the display 204. Thus, in the information processingsystem 1, an UI for identifying the hardware information is displayed onthe display 111.

In the information processing system 1, the input receiver 112 receivesinput for the UI displayed on the display 111 (S11).

Subsequently, the information processing system 1 performs decisionprocessing for a machine learning model according to the hardwareinformation and the test requirement information identified by the inputfor the UI (S12). The details of decision processing for a machinelearning model will be described later.

The information processing system 1 then stores the machine learningmodel decided in the decision processing (S13). The machine learningmodel decided in the decision processing is stored, for instance, in thestorage 103 of the input/output device 200 of the information processingsystem 1.

The details of the decision processing according to the embodiment willbe described with reference to FIGS. 6 and 7.

FIGS. 6 and 7 are each a flowchart illustrating an example of thedetails of the decision processing according to the embodiment.

When the decision processing is started, the controller 120 causes thedecider 130 to decide the configuration of a machine learning model asdescribed above (S101). The decider 130 outputs the decidedconfiguration of a machine learning model to the controller 120.

The controller 120 causes the determiner 140 to perform firstdetermination as to whether the machine learning model obtained from thedecider 130 meets the first performance requirement which is arequirement on hardware performance (S102). The determiner 140determines whether or not a processing speed, for instance, calculatedfrom the configuration of the machine learning model decided by thedecider 130 exceeds the threshold T11. The determiner 140 outputs adetermination result of the first determination to the controller 120.

When the determiner 140 determines that the first performancerequirement is met in the first determination (Yes in S102), thecontroller 120 causes the learner 150 to perform learning using themachine learning model in the configuration determined to meet the firstperformance requirement (S103). Specifically, when the determiner 140determines that a processing speed calculated from the decidedconfiguration of the machine learning model exceeds the threshold T11,the controller 120 causes the learner 150 to perform the above-mentionedlearning. While the learner 150 is executing learning, the interrupter151 obtains a learning situation of the learner 150, and when theaccuracy (error) does not converge in an early stage of learning,learning by the learner 150 may be interrupted. The learner 150 outputsa learned model obtained by the learning to the controller 120.

On the other hand, when the determiner 140 determines that the firstperformance requirement is not met in the first determination (No inS102), the controller 120 causes the decider 130 to change theconfiguration of the machine learning model and decide the configurationof another machine learning model. Specifically, when the determiner 140determines that the processing speed calculated from the decidedconfiguration of the machine learning model is lower than or equal tothe threshold T11, the controller 120 causes the decider 130 to performthe processing in step S101.

When step S103 is completed, the controller 120 causes the determiner140 to perform the second determination as to whether or not the learnedmodel obtained from the learner 150 meets the second performancerequirement which is a requirement on evaluation value of output ofmachine learning model (S104). For instance, the determiner 140determines whether or not the accuracy rate calculated as the evaluationvalue of output of the learned model exceeds the threshold T21. Thedeterminer 140 outputs a determination result of the seconddetermination to the controller 120. That is, when the determiner 140determines that the learned model meets the second performancerequirement in the second determination, the determiner 140 outputs tothe controller 120 information indicating that the first performancerequirement and the second performance requirement are met. It is to benoted that the controller 120 may cause the display 111 to display theinformation.

When the determiner 140 determines that the second performancerequirement is met in the second determination (Yes in S104), thecontroller 120 causes the weight reducer 160 to perform the weightreduction processing on the learned model which has been determined tomeet the second performance requirement (S106). The weight reducer 160outputs a weight reduction model obtained by the weight reductionprocessing to the controller 120.

On the other hand, when the determiner 140 determines that the secondperformance requirement is not met in the second determination (No inS104), the controller 120 causes the determiner 140 to determine whetherthe evaluation value of the output of the learned model meets conditionC2, or the number of determinations, in which it is determined that thesecond performance requirement is not met, is less than N2 (S105). It isto be noted that the second performance requirement is a requirement onaccuracy rate, and a larger value of the accuracy rate indicates higherperformance. A threshold T22 is a value smaller than the threshold T21.Specifically, “the evaluation value of the output of the learned modelmeets condition C2” is, for instance, that the accuracy rate as anevaluation value is higher than or equal to the threshold T22.

On the other hand, when the second performance requirement is for anevaluation value, such as an error or an accuracy rate, in which asmaller value indicates higher performance, the threshold T22 is a valuelarger than the threshold T21. Thus, “the evaluation value of the outputof the learned model meets condition C2” is, for instance, that theerror or the accuracy rate as an evaluation value is lower than or equalto the threshold T22. Thus, the determiner 140 in this case determinesin step S105 whether or not the evaluation value is lower than or equalto the threshold T22, or the number of determinations, in which it isdetermined that the second performance requirement is not met, is lessthan N2.

When the accuracy rate is higher than or equal to the threshold T22, orthe number of determinations, in which it is determined that the secondperformance requirement is not met, is less than N2 (Yes in S105), thecontroller 120 causes the learner 150 to perform the learning in stepS103 again with a parameter different from the one used in thepreviously performed learning. In this case, the controller 120 causesthe learner 150 to perform learning by changing the learning rate of aneural network as a parameter to a different value, for instance.Subsequently, the controller 120 causes the determiner 140 to performthe second determination in step S104 as to whether or not a learnedmodel obtained by learning with the different parameter meets the secondperformance requirement.

When the accuracy rate is lower than the threshold T22, or the number ofdeterminations, in which it is determined that the second performancerequirement is not met, is greater than or equal to N2 (No in S105), thecontroller 120 causes the decider 130 to change the configuration of themachine learning model and decide the configuration of another machinelearning model. In short, in this case, the controller 120 causes thedecider 130 to perform the processing in step S101. Subsequently, thecontroller 120 causes the determiner 140 to perform the firstdetermination in step S102 as to whether or not the machine learningmodel in the changed configuration meets the first performancerequirement.

When step S106 is completed, the controller 120 causes the determiner140 to perform the third determination as to whether or not a weightreduction model obtained from the weight reducer 160 meets the thirdperformance requirement which is a requirement on hardware performance(S107). The determiner 140 determines whether or not a processing speedcalculated from the weight reduction model obtained by the weightreduction processing exceeds the threshold T31, for instance. Thedeterminer 140 outputs a determination result of the third determinationto the controller 120. That is, when the determiner 140 determines thatthe weight reduction model meets the third performance requirement inthe third determination, the determiner 140 outputs to the controller120 information indicating that the third performance requirement ismet. It is to be noted that the controller 120 may cause the display 111to display the information.

When the determiner 140 determines that the third performancerequirement is not met in the third determination (Yes in S107), thecontroller 120 causes the determiner 140 to perform the fourthdetermination as to whether or not the weight reduction model determinedto meet the third performance requirement in the third determinationmeets the fourth performance requirement which is a requirement onhardware performance of real machine (S109). Specifically, thecontroller 120 causes the determiner 140 to measure the hardwareperformance when the processor of a real machine actually executesprocessing using the weight reduction model, and to determine whether ornot the measured hardware performance meets the fourth performancerequirement. The determiner 140 measures a processing speed, forinstance when the processor of a real machine actually executesprocessing using the weight reduction model, and determines whether ornot the measured processing speed exceeds the threshold T41. Thedeterminer 140 outputs a determination result of the fourthdetermination to the controller 120. That is, when the determiner 140determines that the fourth performance requirement is met in the fourthdetermination, the determiner 140 outputs to the controller 120information indicating that the fourth performance requirement is met.

When the determiner 140 determines that the third performancerequirement is not met in the third determination (No in S107), thecontroller 120 causes the determiner 140 to determine whether thehardware performance of the weight reduction model meets condition C3,or the number of determinations, in which it is determined that thethird performance requirement is not met, is less than N3 (S108). It isto be noted that the third performance requirement is a requirement onprocessing speed, and a larger value of processing speed indicateshigher hardware performance. A threshold T32 is a value smaller than thethreshold T31. Specifically, “the hardware performance of the weightreduction model meets condition C3” is, for instance, that theprocessing speed as hardware performance is higher than or equal to thethreshold T32.

On the other hand, when the third performance requirement is forhardware performance in which a smaller value indicates higherperformance like power consumption, the threshold T32 is a value largerthan the threshold T31. Thus, “the hardware performance of the weightreduction model meets condition C3” is, for instance, that the powerconsumption as hardware performance is lower than or equal to thethreshold T32. Thus, the determiner 140 in this case determines in stepS108 whether or not the hardware performance is lower than or equal tothe threshold T32, or the number of determinations, in which it isdetermined that the third performance requirement is not met, is lessthan N3.

When the processing speed is higher than or equal to the threshold T32,and the number of determinations, in which it is determined that thethird performance requirement is not met, is less than N3 (Yes in S108),the controller 120 causes the weight reducer 160 to perform the weightreduction processing again with a parameter different from the one usedin the previously performed weight reduction processing. In this case,the controller 120 causes the weight reducer 160 to change the degree ofweight reduction, as the parameter, of the weight reduction processingto a different degree, and to perform the weight reduction processing,for instance. Subsequently, the controller 120 causes the determiner 140to perform the third determination in step S107 as to whether or not aweight reduction model obtained by the weight reduction processing witha different parameter meets the third performance requirement.

When the processing speed is lower than the threshold T32, or the numberof determinations, in which it is determined that the third performancerequirement is not met, is greater than or equal to N3 (No in S108), thecontroller 120 causes the decider 130 to change the configuration of themachine learning model and decide the configuration of another machinelearning model. In short, in this case, the controller 120 causes thedecider 130 to perform the processing in step S101. Subsequently, thecontroller 120 causes the determiner 140 to perform the firstdetermination in step S102 as to whether or not the machine learningmodel in the changed configuration meets the first performancerequirement.

When the determiner 140 determines that the fourth performancerequirement is not met in the fourth determination (Yes in S109), thecontroller 120 causes the determiner 140 to perform the fifthdetermination as to whether or not the weight reduction model determinedto meet the fourth performance requirement in the fourth determinationmeets the fifth performance requirement which is a requirement onevaluation value of output of the machine learning model (S111). Forinstance, the determiner 140 determines whether or not the accuracy ratecalculated as the evaluation value of output of the weight reductionmodel exceeds the threshold T51. The determiner 140 outputs adetermination result of the fifth determination to the controller 120.That is, when the determiner 140 determines that the weight reductionmodel meets the fifth performance requirement in the fifthdetermination, the determiner 140 outputs to the controller 120information indicating that the fifth performance requirement is met. Itis to be noted that the controller 120 may cause the display 111 todisplay the information.

When the determiner 140 determines that the fourth performancerequirement is not met in the fourth determination (No in S109), thecontroller 120 causes the determiner 140 to determine whether thehardware performance of the weight reduction model meets condition C4,or the number of determinations, in which it is determined that thefourth performance requirement is not met, is less than N4 (S110). It isto be noted that the fourth performance requirement is a requirement onprocessing speed, and a larger value of processing speed indicateshigher hardware performance. A threshold T42 is a value smaller than thethreshold T41. Specifically, “the hardware performance of the weightreduction model meets condition C4” is, for instance, that theprocessing speed as hardware performance is higher than or equal to thethreshold T42.

On the other hand, when the fourth performance requirement is forhardware performance in which a smaller value indicates higherperformance like power consumption, the threshold T42 is a value largerthan the threshold T41. Thus, “the hardware performance of the weightreduction model meets condition C4” is, for instance, that the powerconsumption as hardware performance is lower than or equal to thethreshold T42. Thus, the determiner 140 in this case determines in stepS110 whether or not the hardware performance is lower than or equal tothe threshold T42, or the number of determinations, in which it isdetermined that the fourth performance requirement is not met, is lessthan N4.

When the processing speed is higher than or equal to the threshold T42,and the number of determinations, in which it is determined that thefourth performance requirement is not met, is less than N4 (Yes inS110), the controller 120 causes the weight reducer 160 to perform theweight reduction processing again with a parameter different from theone used in the previously performed weight reduction processing. Inthis case, the controller 120 causes the weight reducer 160 to changethe degree of weight reduction, as the parameter, of the weightreduction processing to a different degree, and to perform the weightreduction processing, for instance. Subsequently, the controller 120causes the determiner 140 to perform the third determination in stepS107 as to whether or not a weight reduction model obtained by theweight reduction processing with a different parameter meets the thirdperformance requirement.

When the processing speed is lower than the threshold T42, or the numberof determinations, in which it is determined that the fourth performancerequirement is not met, is greater than or equal to N4 (No in S110), thecontroller 120 causes the decider 130 to change the configuration of themachine learning model and decide the configuration of another machinelearning model. In short, in this case, the controller 120 causes thedecider 130 to perform the processing in step S101. Subsequently, thecontroller 120 causes the determiner 140 to perform the firstdetermination in step S102 as to whether or not the machine learningmodel in the changed configuration meets the first performancerequirement.

When the determiner 140 determines that the fifth performancerequirement is not met in the fifth determination (Yes in S111), thecontroller 120 decides the weight reduction model determined to meet thefifth performance requirement in the fifth determination as the machinelearning model, and completes the decision processing.

When the determiner 140 determines that the fifth performancerequirement is not met in the fifth determination (No in S111), thecontroller 120 causes the determiner 140 to determine whether theevaluation value of the output of the weight reduction model meetscondition C5, or the number of determinations, in which it is determinedthat the fifth performance requirement is not met, is less than N5(S112). The fifth performance requirement is for an evaluation value inwhich a larger value indicates higher performance, thus a threshold T52is a value smaller than the threshold T51. Thus, “the evaluation valueof the output of the weight reduction model meets condition C5” is, forinstance, that the accuracy rate as the evaluation value is higher thanor equal to the threshold T52.

On the other hand, when the fifth performance requirement is for anevaluation value, such as an error or an accuracy rate, in which asmaller value indicates higher performance, the threshold T52 is a valuelarger than the threshold T51. Thus, “the evaluation value of the outputof the weight reduction model meets condition C5” is, for instance, thatthe error or the accuracy rate as an evaluation value is lower than orequal to the threshold T52. Thus, the determiner 140 in this casedetermines in step S112 whether or not the evaluation value is lowerthan or equal to the threshold T52, or the number of determinations, inwhich it is determined that the fifth performance requirement is notmet, is less than N5.

When the accuracy rate is higher than or equal to the threshold T52, andthe number of determinations, in which it is determined that the fifthperformance requirement is not met, is less than N5 (Yes in S112), thecontroller 120 causes the weight reducer 160 to perform the weightreduction processing again with a parameter different from the one usedin the previously performed weight reduction processing. In this case,the controller 120 causes the weight reducer 160 to change the degree ofweight reduction, as the parameter, of the weight reduction processingto a different degree, and to perform the weight reduction processing,for instance. Subsequently, the controller 120 causes the determiner 140to perform the third determination in step S107 as to whether or not aweight reduction model obtained by the weight reduction processing witha different parameter meets the third performance requirement.

When the accuracy rate is lower than the threshold T52, or the number ofdeterminations, in which it is determined that the fifth performancerequirement is not met, is greater than or equal to N5 (No in S112), thecontroller 120 causes the determiner 140 to determine whether or not thenumber of determinations, in which it is determined that the fifthperformance requirement is not met, is greater than or equal to N6(S113). It is to be noted that N6 is a value greater than N5.

When the determiner 140 determines that the number of determinations, inwhich it is determined that the fifth performance requirement is notmet, is greater than or equal to N6 (Yes in S113), the controller 120changes the hardware configuration (S114). For instance, the controller120 may cause the display 111 to display instructions for prompting auser to change the configuration of a target processor because anoptimal machine learning model for a designated configuration of aprocessor has not been found. Alternatively, the controller 120 mayperform processing to automatically change the configuration of a targetprocessor by a predetermined algorithm. Also, the controller 120 maychange the first performance requirement, the third performancerequirement, and the fourth performance requirement on hardwareperformance to a less stringent performance requirement.

When the determiner 140 determines that the number of determinations, inwhich it is determined that the fifth performance requirement is notmet, is less than N6 (No in S113), the controller 120 causes the decider130 to change the configuration of the machine learning model and decidethe configuration of another machine learning model. In short, in thiscase, the controller 120 causes the decider 130 to perform theprocessing in step S101. Subsequently, the controller 120 causes thedeterminer 140 to perform the first determination in step S102 as towhether or not the machine learning model in the changed configurationmeets the first performance requirement.

[1-5. Effect]

With the information processing system 1 according to the embodiment,the configuration of a machine learning model is changed withoutperforming the second determination until the first performancerequirement is met in the first determination, and thus it is possibleto reduce the number of times of performing the second determination.Therefore, it is possible to efficiently decide a machine learningmodel. In addition, it is possible to reduce the processing load andconsumption energy of processors.

With the information processing system 1 according to the embodiment,when it is determined N2 times or more that the second performancerequirement is not met, the configuration of the machine learning modelis changed, and thus the number of times of learning can be reduced.Therefore, it is possible to efficiently decide a machine learningmodel.

With the information processing system 1 according to the embodiment,the configuration of the machine learning model is changed withoutperforming the second determination, the weight reduction processing,and the third determination until the first performance requirement ismet in the first determination. Therefore, it is possible to reduce thenumber of times of performing the second determination, the weightreduction processing, and the third determination.

In the information processing system 1 according to the embodiment, thecondition for hardware performance specified in the third performancerequirement is more stringent than the condition for hardwareperformance specified in the first performance requirement. Therefore,it is possible to reduce the number of times of determination that thefirst performance requirement is not met, decision of the configurationof the machine learning model, and performing the first determination.Also, more stringent determination is performed in the thirddetermination, thereby making it possible to reduce the number of timesof performing subsequent processing to the third determination.

In the information processing system 1 according to the embodiment,since the determination in step S108 is performed, even when the thirdperformance requirement is not met, when the current performance meets arequirement close to the third performance requirement, the weightreduction processing is performed with a changed parameter. Therefore,it is possible to efficiently determine a weight reduction model thatmeets the third performance requirement.

In the information processing system 1 according to the embodiment, whenthe third performance requirement is not met, and the number ofdeterminations, in which the third performance requirement is not met,is greater than or equal to N3, the configuration of the machinelearning model is changed. Therefore, it is possible to reduce thenumber of times of performing the weight reduction processing. Thus, itis possible to efficiently decide a machine learning model.

With the information processing system 1 according to the embodiment,the fourth determination is performed on the weight reduction modelwhich has been determined to meet the third performance requirement inthe third determination, and thus it is possible to reduce the number oftimes of performing the fourth determination.

In the information processing system 1 according to the embodiment,since the determination in step S110 is performed, even when the fourthperformance requirement is not met, when the current performance meets arequirement close to the fourth performance requirement, the weightreduction processing is performed with a changed parameter, thus it ispossible to efficiently determine a weight reduction model that meetsthe fourth performance requirement.

In the information processing system 1 according to the embodiment, whenthe fourth performance requirement is not met, and the number ofdeterminations, in which the fourth performance requirement is not met,is greater than or equal to N4, the configuration of the machinelearning model is changed. Therefore, it is possible to reduce thenumber of times of performing the weight reduction processing. Thus, itis possible to efficiently decide a machine learning model.

With the information processing system 1 according to the embodiment,the fifth determination is performed on the weight reduction model whichhas been determined to meet the fourth performance requirement in thefourth determination, and thus it is possible to reduce the number oftimes of performing the fifth determination.

In the information processing system 1 according to the embodiment,since the determination in step S112 is performed, even when the fifthperformance requirement is not met, when the current performance meets arequirement close to the fifth performance requirement, the weightreduction processing is performed with a changed parameter, thus it ispossible to efficiently determine a weight reduction model that meetsthe fifth performance requirement.

In the information processing system 1 according to the embodiment, whenthe fifth performance requirement is not met, and the number ofdeterminations, in which the fifth performance requirement is not met,is greater than or equal to N5, the configuration of the machinelearning model is changed. Therefore, it is possible to reduce thenumber of times of performing the weight reduction processing. Thus, itis possible to efficiently decide a machine learning model.

[1-6. Modification]

Although the information processing system 1 according to the embodimentis configured to include the information processing device 100 and theinput/output device 200, without being limited to this, the informationprocessing system 1 may include one information processing device. Inthis case, the hardware configuration of the one information processingdevice may be the same as the hardware configuration of the input/outputdevice 200.

In the information processing system 1 according to the embodiment, whenthe configuration of a machine learning model is changed, the decider130 automatically changes the machine learning model to another machinelearning model. However, without being limited to this, when theconfiguration of a machine learning model is changed, the controller 120may cause the display 111 to display a prompt to change theconfiguration of the machine learning model, and to display an UI thatreceives an instruction for changing the configuration of the machinelearning model. In this case, a user may input an instruction to the UIusing the input IF 205, and the input receiver 112 may receive a machinelearning model in a changed configuration. The input receiver 112 mayoutput the machine learning model in a changed configuration to thecontroller 120 which may cause the determiner 140 to perform the firstdetermination on the obtained machine learning model.

Although the information processing system 1 according to the embodimentuses a neural network for machine learning, without being limited tothis, a decision tree, the Random Forest, a k-nearest neighbor methodmay be used. When the RandomForest is used, a hyper-parameter is decidedas the configuration of a machine learning model. A hyper-parameter isdecided by factors such as the number of decision trees, the depth ofeach decision tree, and the number of leaves (the number of categoriesof each node). When the number of decision trees, the depth of eachdecision tree, and the number of leaves of a hyper-parameter areincreased, the representational ability can be improved. However, theamount of calculation is increased accordingly.

Bit quantization may be used as the weight reduction processing in thiscase. In the Random Forest, processing to eliminate a decision tree witha low contribution or processing to eliminate part of the nodes orleaves in a decision tree may be performed as the weight reductionprocessing.

In the information processing system 1 according to the embodiment,processing to decide and change a network configuration is performed inthe decider 130. However, the processing may not be performed. Forinstance, the following information processing methods may be performed.

The following processing is performed using a processor. Learning isperformed using a machine learning model. The second determination as towhether or not a learned model obtained by the learning meets the secondperformance requirement which is a requirement on evaluation value ofoutput of the machine learning model. When it is determined in thesecond determination that the learned model meets the second performancerequirement, weight reduction processing is performed on the learnedmodel which has been determined to meet the second performancerequirement in the second determination. Subsequently, the thirddetermination as to whether or not a weight reduction model obtained bythe weight reduction processing meets the third performance requirementwhich is a requirement on hardware performance. Then the fourthdetermination as to whether or not the weight reduction model determinedto meet the third performance requirement in the third determinationmeets the fourth performance requirement which is a requirement onhardware performance of real machine. When it is determined that thefourth performance requirement is met in the fourth determination,information indicating that the fourth performance requirement is met isoutput, and when the hardware performance of the weight reduction modeldetermined not to meet the third performance requirement in the thirddetermination meets the first condition, that is, the condition C3, theweight reduction processing is performed with a parameter different fromthe one used in the previously performed weight reduction processing.

Even in this case, when it is determined that the third performancerequirement is not met in the third determination, the weight reductionprocessing may be performed again, it is possible to reduce the numberof times of performing the fourth determination as to whether or not theweight reduction model determined to meet the fourth performancerequirement which is a requirement on hardware performance of realmachine.

It is to be noted that in the embodiment, each component may beimplemented by dedicated software, or by executing a software programrelevant to the component. Each component may be implemented by aprogram executor, such as a CPU or a processor, that reads and executesa software program recorded on a recording medium such as a hard disk ora semiconductor memory. Here, software that implements the informationprocessing method, and the information processing system in theembodiment is the program as described below.

Specifically, the program causes a computer to execute an informationprocessing method including: deciding a configuration of a machinelearning model using a processor; performing first determination usingthe processor as to whether the machine learning model in the decidedconfiguration meets a first performance requirement which is arequirement on hardware performance; when it is determined that thefirst performance requirement is met in the first determination,performing learning using the processor and the machine learning modelin the configuration determined to meet the first performancerequirement, performing second determination using the processor as towhether a learned model obtained by the learning meets a secondperformance requirement which is a requirement on evaluation value ofoutput of a machine learning model, and when the learned model isdetermined to meet the second performance requirement in the seconddetermination, outputting information using the processor, theinformation indicating that the first performance requirement and thesecond performance requirement are met; and when it is determined thatthe first performance requirement is not met in the first determination,changing the configuration of the machine learning model using theprocessor, and performing the first determination as to whether themachine learning model in the changed configuration meets the firstperformance requirement.

Although an information processing method and an information processingsystem according to one or more aspects of the present disclosure havebeen described based on the embodiment above, the present disclosure isnot limited to the embodiment. The embodiment to which variousmodifications which will occur to those skilled in the art are made, andan embodiment constructed by a combination of components of differentembodiments without departing from the spirit of the present disclosureare also included within the scope of the present disclosure.

The present disclosure is useful for an information processing methodcapable of efficiently deciding a machine learning model that meetsrequested hardware performance requirements.

What is claimed is:
 1. An information processing method for determiningcompatibility of a remote computer for executing a machine learningmodel stored at a server, the method comprising: acquiring, via anetwork and at the server having a processor, hardware information ofthe remote computer; deciding, based on the hardware information of theremote computer, a configuration of the machine learning model stored atthe server that is to be transmitted to and executed at the remotecomputer, using the processor, wherein the configuration of the machinelearning model is capable of being modified to a different configurationin the server in accordance with the hardware information of the remotecomputer; performing a first determination using the processor as towhether the machine learning model in the decided configuration meets afirst performance requirement which is a requirement on hardwareperformance; when it is determined that the first performancerequirement is met in the first determination, performing a machinelearning processing using the processor and the machine learning modelin the configuration determined to meet the first performancerequirement, performing a second determination using the processor as towhether a subsequent machine learned model obtained by the machinelearning processing meets a second performance requirement which is arequirement on an evaluation value of an output of the machine learningmodel, and when the subsequent machine learned model is determined tomeet the second performance requirement in the second determination,outputting results information using the processor, the resultsinformation indicating that the first performance requirement and thesecond performance requirement are met; and when it is determined thatthe first performance requirement is not met in the first determination,changing, via the processor of the server and based on the hardwareinformation of the remote computer, the configuration of the machinelearning model to the different configuration that is capable of beingperformed at the remote computer, wherein the configuration of themachine learning model is changed by modifying a number of hierarchiesor units included in the machine learning model, and performing thefirst determination again as to whether the different configuration ofthe machine learning model meets the first performance requirement. 2.The information processing method according to claim 1, wherein when itis determined that the second performance requirement is not met in thesecond determination, the machine learning processing is performed usinga parameter different from a parameter used in the previously performedmachine learning processing, the second determination is performed as towhether a machine learned model obtained by the machine learningprocessing using the different parameter meets the second performancerequirement, when a number of determinations, in which it is determinedthat the second performance requirement is not met, is greater than orequal to a first number, the configuration of the machine learning modelis changed, and the first determination is performed as to whether themachine learning model in the changed configuration meets the firstperformance requirement.
 3. The information processing method accordingto claim 1, wherein a weight reduction processing is further performedon the subsequent machine learned model determined to meet the secondperformance requirement in the second determination, third determinationis performed as to whether a weight reduction model obtained by theweight reduction processing meets a third performance requirement, whichis a requirement on hardware performance, and when it is determined thatthe third performance requirement is met in the third determination,results information indicating that the third performance requirement ismet is output.
 4. The information processing method according to claim3, wherein a condition for the hardware performance specified in thethird performance requirement is a condition for hardware performancethat is different from a condition for the hardware performancespecified in the first performance requirement.
 5. The informationprocessing method according to claim 3, wherein when hardwareperformance of the weight reduction model determined not to meet thethird performance requirement in the third determination meets a firstcondition, the weight reduction processing is performed using aparameter different from a parameter used in the previously performedweight reduction processing, and the third determination is performed asto whether a weight reduction model obtained by the weight reductionprocessing using the different parameter meets the third performancerequirement.
 6. The information processing method according to claim 3,wherein (i) when hardware performance of the weight reduction modeldetermined not to meet the third performance requirement in the thirddetermination does not meet the first condition, or (ii) when a numberof determinations, in which it is determined that the third performancerequirement is not met, is greater than or equal to a second number, theconfiguration of the machine learning model is changed, and the firstdetermination is performed as to whether the machine learning model inthe changed configuration meets the first performance requirement. 7.The information processing method according to claim 3, wherein a fourthdetermination is performed as to whether the weight reduction modeldetermined to meet the third performance requirement in the thirddetermination meets a fourth performance requirement, which is arequirement on hardware performance of a real machine, and when it isdetermined that the fourth performance requirement is met in the fourthdetermination, results information indicating that the fourthperformance requirement is met is output.
 8. The information processingmethod according to claim 7, wherein when the hardware performance ofthe weight reduction model determined not to meet the fourth performancerequirement in the fourth determination meets a second condition, theweight reduction processing is performed using a parameter differentfrom a parameter used in the previously performed weight reductionprocessing.
 9. The information processing method according to claim 7,wherein (i) when the hardware performance of the weight reduction modeldetermined not to meet the fourth performance requirement in the fourthdetermination does not meet the second condition, or (ii) when a numberof determinations, in which it is determined that the fourth performancerequirement is not met, is greater than or equal to a third number, theconfiguration of the machine learning model is changed, and the firstdetermination is performed as to whether the machine learning model inthe changed configuration meets the first performance requirement. 10.The information processing method according to claim 7, wherein a fifthdetermination is performed as to whether the weight reduction modeldetermined to meet the fourth performance requirement in the fourthdetermination meets a fifth performance requirement, which is arequirement on an evaluation value of an output of a machine learningmodel, and when it is determined that the fifth performance requirementis met, results information indicating that the fifth performancerequirement is met is output.
 11. The information processing methodaccording to claim 10, wherein when an evaluation value of the weightreduction model determined not to meet the fifth performance requirementin the fifth determination meets a third condition, the weight reductionprocessing is performed using a parameter different from a parameterused in the previously performed weight reduction processing.
 12. Theinformation processing method according to claim 10, wherein (i) whenthe evaluation value of the weight reduction model determined not tomeet the fifth performance requirement in the fifth determination doesnot meet the third condition, or (ii) when a number of determinations,in which it is determined that the fifth performance requirement is notmet, is greater than or equal to a fourth number, the configuration ofthe machine learning model is changed, and the first determination isperformed as to whether the machine learning model in the changedconfiguration meets the first performance requirement.
 13. Aninformation processing system that stores a machine learning model anddetermines compatibility of a remote computer for executing the machinelearning model, the information processing system comprising aprocessor, wherein the processor acquires, via a network, hardwareinformation of the remote computer; decides, based on the hardwareinformation of the remote computer, a configuration of the machinelearning model that is to be transmitted and executed at the remotecomputer, wherein the configuration of the machine learning model iscapable of being modified to a different configuration in theinformation processing system in accordance with the hardwareinformation of the remote computer; performs a first determination as towhether the machine learning model in the decided configuration meets afirst performance requirement which is a requirement on hardwareperformance; when it is determined that the first performancerequirement is met in the first determination, performs a machinelearning processing using the machine learning model in theconfiguration determined to meet the first performance requirement,performs a second determination as to whether a subsequent machinelearned model obtained by the machine learning processing meets a secondperformance requirement which is a requirement on an evaluation value ofan output of the machine learning model, and when the subsequent machinelearned model is determined to meet the second performance requirementin the second determination, outputs results information indicating thatthe first performance requirement and the second performance requirementare met; and when it is determined that the first performancerequirement is not met in the first determination, changes, based on thehardware information of the remote computer, the configuration of themachine learning model to the different configuration that is capable ofbeing performed at the remote computer, wherein the configuration of themachine learning model is changed by modifying a number of hierarchiesor units included in the machine learning model, and performs the firstdetermination again as to whether the different configuration of themachine learning model meets the first performance requirement.